Subway network fragile line identification method based on Richi curvature

Abstract

The invention discloses a subway network fragile line identification method based on Richi curvature, and the method comprises the following steps: building a subway network based on subway line dataand station passenger flow volume data, and enabling the passenger flow volume to serve as a weight to be assigned to a station; calculating a Richi curvature value of a line in the subway network; and sorting the lines according to a negative-to-positive sequence of the Richi curvature values, sequentially removing the lines in the subway network, and calculating the change of the relative size of the maximum connected subgraph so as to identify the fragile line. The invention provides a method for extracting the fragile line of the subway network from the perspective of flow transmission bycombining actual passenger flow data, and solves the problems that most of the existing methods analyze the fragile line only from the topological structure of the subway network, so that the actual passenger flow distribution condition of each station in actual passenger flow transmission of a line and the flow transmission characteristics in a subway network are not fully considered; and the invention is high in universality.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method for identifying fragile lines in a subway network based on Ritchie curvature. Background technique [0002] In the transportation system, the subway system has become the key to solve the problem of urban traffic congestion due to its fast speed, punctuality and large capacity. A subway system network consists of a set of fixed lines with distributed stations, and passengers travel through line interactions between stations, so it can naturally be analyzed as a network. [0003] At present, the research on the complexity of the subway system has been relatively systematic, but the research mainly focuses on the statistical perspective, mainly analyzing the connection characteristics of the stations in the subway system, while ignoring the actual passenger flow transmission capacity between the stations. When studying the passenger flow transmission ca...

AI Technical Summary

Problems solved by technology

At present, most of the existing methods for identifying vulnerable lines in the subway network only identify vulnerable lines based on the topology of the subway network, and do not fully consider the actual traffic transmission capacity between stations.

Images